BTC Sentiment Wave: Analyzing Bitcoin Sentiment with Wavelet Transforms

Abstract

The BTC Sentiment Wave (BTCSW) is a novel approach to analyzing the sentiment of Bitcoin discussions across various social media platforms using wavelet transforms. This method leverages the multi-scale nature of wavelets to capture sentiment trends at different time scales, providing a more nuanced view of market sentiment than traditional methods. This paper introduces the BTCSW methodology, its implementation, and an analysis of its effectiveness in predicting market movements.

Introduction

Bitcoin, as the leading cryptocurrency, has seen significant fluctuations in its value over the years. One of the key factors influencing these fluctuations is market sentiment. Traditional sentiment analysis methods, such as natural language processing (NLP), have been employed to gauge investor sentiment from textual data. However, these methods often lack the temporal resolution needed to capture rapid changes in sentiment.

The BTC Sentiment Wave introduces a wavelet-based approach to sentiment analysis, which allows for the simultaneous analysis of sentiment at multiple time scales. This is particularly useful in the volatile cryptocurrency market where sentiment can shift rapidly.

Methodology

Data Collection

Data is collected from various social media platforms including Twitter, Reddit, and Bitcoin forums. The data includes posts, comments, and reactions that are relevant to Bitcoin.

Preprocessing

Text data is cleaned and preprocessed to remove noise and irrelevant information. This includes tokenization, stop-word removal, and stemming.

Sentiment Analysis

The preprocessed text is then analyzed using NLP techniques to determine the sentiment of each piece of text. Common sentiment scores such as positive, negative, and neutral are assigned.

Wavelet Transforms

The sentiment scores are then subjected to a wavelet transform. Wavelet transforms are chosen for their ability to analyze data at different scales, which is crucial for capturing the varying time scales of sentiment changes in the market.

Multi-Scale Analysis

The wavelet transform decomposes the sentiment data into multiple scales, allowing for the analysis of sentiment trends at different time frames. This multi-scale analysis provides a comprehensive view of sentiment dynamics.

Implementation

The BTC Sentiment Wave is implemented using Python with libraries such as NumPy for numerical operations, NLTK for NLP tasks, and PyWavelets for wavelet transformations.

Algorithm

1. Collect and preprocess data from social media.
2. Perform sentiment analysis to generate sentiment scores.
3. Apply wavelet transform to the sentiment scores.
4. Analyze the transformed data at different scales.
5. Aggregate the results to generate a sentiment wave profile.

Results

The BTC Sentiment Wave analysis was applied to historical Bitcoin data from 2017 to 2023. The results showed a strong correlation between the sentiment wave profiles and actual market movements. The multi-scale analysis provided insights into both short-term and long-term sentiment trends, which were not evident with traditional methods.

Discussion

The BTC Sentiment Wave offers a new perspective on market sentiment analysis. Its multi-scale approach allows for a more detailed understanding of sentiment dynamics, which can be crucial for investors and traders. However, the method also has limitations, such as the need for high-quality data and the complexity of the analysis.

Conclusion

The BTC Sentiment Wave is a promising tool for analyzing Bitcoin sentiment. Its wavelet-based approach provides a nuanced view of market sentiment that can aid in predicting market movements. Future work will focus on improving the algorithm and expanding its application to other cryptocurrencies.

References

[1] Daubechies, I. (1992). Ten Lectures on Wavelets. SIAM.

[2] Mallat, S. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3] Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining Text Data (pp. 415-463). Springer, Boston, MA.